Express Scripts · Healthcare · St. Louis, MO · 14 weeks
3 stalled ML pilots restarted
Three machine learning pilots had stalled for more than a year because member identity data was inconsistent. We implemented unified entity resolution using Databricks and rebuilt the feature engineering pipeline from the ground up. This cleared the data issues and allowed all three pilots to move into production.
Challenge
Express Scripts had three different member ID systems that never aligned. Their ML models only matched 75% of records correctly, causing about $4.8M a year in misrouted claims and extra manual work. Three AI projects were stuck for over a year because internal teams couldn’t prioritize fixing the data issues.
Outcome
We improved member match accuracy to 94% on the validation set. Within four weeks of delivery, the client restarted all three ML pilots. Reducing misrouting is expected to save $4.8 million annually. The internal ML team now fully owns the pipeline, supported by complete documentation.
Results
- $4.8M Annual claims recovery
- 75 to 94 of 100 Member match accuracy
- 3 ML pilots restarted
- 14 wks Delivery timeline
We had $2 million stuck in three machine learning pilots that weren’t moving forward because our member data was all over the place. Thinklytics cleaned up the data in 14 weeks, and now all three pilots are up and running.